Avoiding dazzling of persons by a light source

ABSTRACT

In a method for avoiding dazzling of a person ( 10 ) by a light source ( 6 ) arranged in an interior ( 20 ) of a vehicle ( 22 ), wherein the light source ( 6 ) during operation emits light ( 24 ) within a beam cone ( 26 ), a camera ( 4 ) is arranged in the interior ( 20 ) and oriented such that at least one monitoring section ( 28 ) of the beam cone ( 26 ), in which the person ( 10 ) can enter, is located in the field of view ( 30 ) of the camera ( 4 ), the camera ( 4 ) records a camera image ( 32 ), using machine person detection, it is ascertained from the camera image ( 32 ) whether at least one part of the person ( 10 ) is located within the beam cone ( 26 ), in this case, at least the region ( 18 ) of the beam cone ( 26 ) in which the part of the person ( 10 ) is located is switched to glare-free.

BACKGROUND OF THE INVENTION

The invention relates to dazzling of a person by a light source, whereinthe light source is arranged in an interior of a vehicle.

DISCUSSION OF THE PRIOR ART

DE 10 2016 006 703 A1 discloses a projector for producing a lightprojection in an interior of an aircraft as a vehicle, in particular inthe passenger cabin as an interior. It is to be avoided in practice thatpersons, e.g. passengers in a corresponding aircraft, are dazzled bylight emitted by the projector.

SUMMARY OF THE INVENTION

The present invention is directed to improving the situation with suchlight sources. More particularly, the present invention is directed to amethod serving to avoid a person being dazzled by a light source. Thelight source is arranged in an interior of a vehicle, wherein the lightsource during operation emits light within a beam cone. In the method, acamera is arranged in the interior and oriented such that at least onemonitoring section, that is to say a part, of the beam cone into whichthe or a person can or could enter lies within the field of view of thecamera.

The camera then records a camera image. Using machine person detection,it is then ascertained from the camera image whether at least one partof the person is located within the beam cone or in the monitoringsection. If at least the part of the person is located within the beamcone or monitoring section, at least the region of the beam cone inwhich the part of the person is located is switched to glare-free.

The method relates to persons that are located in the interior of thevehicle and can potentially enter the beam cone and thus run the risk ofbeing dazzled by the light source. The light source is in particular aprojector. The light source is in particular installed in the interior.

The beam cone describes the spatial region within which the light sourcecan potentially emit light. Depending on the light source and theoperating state, a possible actual emission of light then takes placeentirely or partially within the beam cone. A “cone” is here understoodto mean, in a broad mathematical sense, a body having a base area of anydesired shape, wherein the cone here in particular has a rectangularbase area that corresponds to the maximum image content of a projectoras a light source presented on a target area.

It is detected, owing to the method, using machine person detectionwhether a person is located within the beam cone of the light source andwould thus be dazzled during normal operation of the light source.However, actual glare is then avoided by virtue of the beam cone beingcorrespondingly switched to glare-free at least at the location of theperson, at least the head thereof, at least the eyes thereof.

“Switching to glare-free” means that the light of the light source islimited in its intensity to a maximum that is no longer perceived as“glare” by the person. It is possible to base this on averageperception, for example. A correspondingly reduced degree of brightnesscan be individually ascertained here, for example by way of tests,estimates, etc., depending on the properties of the light source, theinstallation situation, the minimum distance of any persons from thelight source as intended and so on. The light intensity can here beselected for example in the range of smaller than 3, at most 4, at most5, at most 6 or at most 7 or at most 8 or at most 9 of the de Boer scale(see inter alia: “Blendung—Theoretischer Hintergrund, Informationen desInstituts für Arbeitsschutz der DGUV” (Glare—Theoretical Background,Information of the Institute for Occupational Safety and Health of theGerman Social Accident Insurance), IFA Institut für Arbeitsschutz derDeutschen Gesetzlichen Unfallversicherung (Institute for OccupationalSafety and Health of the German Social Accident Insurance), May 2010,page 9, table 1, retrieved from“https://www.dguv.de/medien/ifa/de/fac/strahl/pdf/blendung_theorie.pdf”on 30 Oct. 2018).

In one preferred embodiment, the entire beam cone is switched toglare-free when at least part of the person is located within the beamcone. This is particularly simple since the beam cone becomesdazzle-free as a whole, or the light source becomes dazzle-free for theentire beam cone. Selective operation or selective switching-off orblocking of the light source for specific parts of the beam cone is thusavoided.

In a preferred embodiment, the region of the beam cone is switched toglare-free by way of the light source being switched to glare-free orbeing switched off only for the relevant region. The region is then theregion of the person, that is to say for example the outline of saidperson, the head thereof or the eyes thereof. The brightness of thelight source is thus locally reduced to a glare-free degree or set tozero. A corresponding switch of the light source to glare-free can beeffected particularly easily.

In a preferred embodiment, the beam cone is aimed at a target area ofthe interior, and the camera is oriented such that at least a partialregion of the target area lies within the field of view of the camera.In particular, the field of view of the camera thus contains at least apartial region of the target area and in particular also a residualportion that does not image the target area. The beam cone of the lightsource terminates at the target area. Many situations in which personscan be potentially located on purpose more in the region of the targetarea than in the region of the light source, and are therefore at riskfrom glare, are conceivable in practice. Such situations can beparticularly efficiently managed with the present embodiment.

The target area is in particular a bin in a passenger cabin of anaircraft. Said bin is generally located at head level of adults ofaverage height. The risk of glare is therefore particularly an issuehere.

In a preferred variant of this embodiment, the—in particular entire orwhole—partial region of the target area that is captured by the camerais not taken into account in machine vision or in machine persondetection. In particular, the entire partial region is not taken intoaccount. Consequently, in particular only the residual part of thecamera image that does not correspond to the partial region isevaluated, that is to say in particular a vicinity of the partial regionor another region adjacent to the partial region.

In particular, in a further variant, it is not the entire partial regionof the target area but only part thereof that is not taken into account;in particular, the partial region is not left out in its entirety,specifically a partial region with persons and/or movements as part ofthe image content presented on the target area by the light source, inparticular a projector. In this way, it is generally—or in this variantpurposefully—avoided that persons and/or movements that are part of theimage content produced by the light source are incorrectly detected asparts of persons at risk of glare. In particular, it is possible herefor the entire target area irradiated by the light source not to betaken into account in machine vision or machine person detection. Thisis particularly simple to accomplish.

Alternatively, only the parts of the target area on which correspondingimage contents are presented are excluded from machine vision or frommachine person detection. However, real persons located within the beamcone of the light source can continue to be detected therewith. In otherwords, the part of a person that is presented here on the target area bythe light source is in particular recognized as being part of the imagecontent and the beam cone in this case is not switched to glare-free.

In a preferred embodiment, the camera is oriented such that a vicinityof the beam cone (adjoining the beam cone) also lies in the field ofview of the camera. In this way, it is also possible to detect personsas soon as they approach the beam cone. In this way, it is possible toswitch the beam cone to glare-free as a precaution before the personenters it. Glare can thus be prevented at any time. It is also possibleto extrapolate a person entering the beam cone into said beam cone whenthe target area is excluded from person detection (for example onaccount of reproduced image contents etc., see above).

In a preferred embodiment, the camera is placed next to the lightsource. This installation position for a camera is particularlyadvantageous because in this way the beam cone and/or possibly also thetarget area or the part of the target area that is illuminated by thelight source is completely or substantially completely capturable by thecamera.

In a preferred embodiment, machine vision and/or face detection and/ortexture detection and/or movement detection is/are performed as part ofthe machine person detection. The corresponding methods are particularlysuitable for machine person detection.

The present invention is further directed to the use of the machineperson detection in a method in accordance with the present invention.The use and at least some of the embodiments thereof and the respectiveadvantages have already been explained analogously in connection withthe method according to the invention.

The present invention is based on the following findings, observationsor considerations and also includes the following embodiments. Theembodiments are in this case also referred to as “the invention”, partlyfor the purposes of simplification. The embodiments may in this casealso contain parts or combinations of the abovementioned embodiments orcorrespond to them and/or possibly also include embodiments which havenot yet been mentioned.

The invention is based on the idea of integrating light sources(projectors) in the aircraft cabin to project light (images) onto atarget area (the surfaces for example of bins or monuments). In manyintegration scenarios, it is here not possible to prevent passengersfrom moving in the light beam of the projector and potentially beingdazzled in this way (line of sight situation). In order to neverthelessbe able to utilize these integration scenarios without negativelyimpacting the passenger, the invention proposes a method that switchesoff or blocks the light beam depending on the situation if a passengerenters the glare region. “Blocking” in this case can in particular alsobe understood to mean to switch off the light beam locally in the regionof the face only in the case of face detection.

The invention is based on the consideration that it should go beyondpurely avoiding the glare situation, that is to say by correspondingintegration of the projectors.

The invention is based on the idea of achieving the object by usingmachine vision. In addition to the system projector-target area, acamera which can capture the region of the projected image and theimmediate environment thereof is installed to this end. Based on variousmethods such as face detection, texture detection or movement detection,it is ascertained here whether a person is located in the region that isat risk of glare (in particular in the beam cone). Depending on themethod, the entire light source can be deactivated thereupon or therelevant image region that would cause glare can be blocked. It is alsopossible to take into account the fact that persons or movementsexisting in the projected image content are not incorrectly detected asa glare situation and result in the light source being switched off.

The advantage of the method is that, using the described method, targetareas or projection areas or integration scenarios (generallyillumination situations) that would otherwise result in passengers beingdazzled can also be used.

The invention has two core aspects: first, the use of computer vision toavoid glare in projection and light systems in the aircraft cabin.Secondly, avoiding erroneous detections, which could occur due tohuman-like elements in the projected image content.

According to the present invention, a functionality has been developedin the field of projection system control to detect by way of camerawhether a person is dazzled by the projector. In the case of glare, theprojector is then to be deactivated or the corresponding region in theimage is to be blocked. The invention thus describes camera-controlledavoidance of the dazzling of persons.

Further, according to the present invention, the avoidance of thedazzling of passengers is accomplished by machine person detection.Possible dazzling of the passengers by projectors/light sources in theaircraft cabin is avoided or reduced by machine person detection, inparticular by machine vision (computer vision).

BRIEF DESCRIPTION OF THE DRAWINGS

Further features, effects and advantages of the invention becomeapparent from the following description of one preferred exemplaryembodiment of the invention and the appended figures. In the figures, ineach case in a schematic diagram:

FIG. 1 shows convolution masks according to the prior art (Viola &Jones, 2001),

FIG. 2 shows a cascade of the classifiers according to the prior art(Viola & Jones, 2001),

FIG. 3 shows a diagram of a test setup according to the invention,

FIG. 4 shows a spatial view of the test setup of FIG. 1,

FIG. 5 shows an illustration of matches,

FIG. 6 shows a difference image in projection,

FIG. 7 shows a difference image around projection,

FIG. 8 shows a feature image,

FIG. 9 shows a structure image,

FIG. 10 shows face detection

DETAILED DESCRIPTION OF THE INVENTION

According to the present invention, a system detects when a person islocated between a projector and the area that is projected by saidprojector. This is expedient when the intention is to avoid that theperson is inconveniently dazzled. Therefore, it is the objective in sucha case to switch off the projector, or in other versions to switch offthe regions of the projector in which the person is located.

A plurality of approaches are pursued here, which in each case meet theconditions in the aircraft cabin in different ways, because owing tovibrations and changes in brightness, there are many limitations inimage processing.

First, the theoretical bases of image processing relating to the variousapproaches will be discussed. Next, a test setup with the componentsused and software will be described. Based thereon, five approaches willbe discussed, “detecting in the projection”, “detecting around theprojection” with difference image, feature and structure detection and“detecting using face detection”. Finally, the results of the individualapproaches will be evaluated.

Basic Principles:

Feature Detection:

The purpose of the feature detection is to re-identify a given imagefeature in a different image. Re-identification should be independenthere of translation, rotation and scaling. Both images are thereforesearched for points of interest which are compared to one another suchthat re-identification is possible.

KAZE

KAZE is an algorithm for feature detection developed by Pablo F.Alcantarilla, Jesus Nuevo and Adrien Bartoli. This algorithm uses theHessian matrix for detecting the points of interest. Here, a search isperformed for positions at which the determinant of the Hessian matrixreaches a local maximum. These points are generally of interest becausethey contain sufficient structure for them to be re-identified in thecomparison image. Furthermore, a scale space is created to make possiblea scaling invariance. Scale space means that the output image isfiltered multiple times with different filter sizes and consequently theimage is present in a plurality of scalings. To ensure re-identificationin the comparison image, a description of said points of interest isrequired. This is accomplished with a 64-dimensional feature vector,which is composed of brightness and gradient information and isinvariant with respect to scaling, rotation and translation. Using asearch algorithm, a correspondence between output and comparison imageis then established. (Alcantarilla, Nuevo, & Bartoli, 2012.

Ransac:

In order to minimize incorrect assignments, the RANSAC method is used.In this case, a random subset is selected from all the assignments andthe number of assignments that are consistent with said solution isdetermined. Once a subset has been found that does not contain outliers,this set is particularly large. (Kunz, Bildverarbeitung SS 2016 chapter20, 2016).

Difference Image:

In the case of the difference image, pixel-wise subtraction of twoimages with respect to one another is effected. Grey-level images arenecessary herefor.g′(x,y)=g1(x,y)−g2(x,y)with g1(x,y), g2(x,y)=input image and g′(x,y)=output image.

It is possible with this method to discern differences between twoimages. Since movement in image processing is a change in the greyvalues, it is also possible to detect movement with this method. So, ifg1(x,y) is the image with the person and g2(x,y) is an image without theperson, this means that all pixel values that are not zero include achange (Erhardt, 2008). If now a threshold is also established and thenewly created output image is binarized, the result is a black-and-whitemask that reproduces only the changes.

Since a change in the brightness and movement of the camera also means achange in grey level, this method is not robust with respect tobrightness changes and movement of the camera.

Mixture of Gaussian:

To counteract changes in the background image and vibrations, it ispossible to render the background model adaptive. OpenCV to this endmakes available the extended difference image method “mixture ofGaussian” (MOG). It is based on the paper by Chris Stauffer and W. E. LGrimson (Chris Stauffer, 1999). This method is highly suitable in thecase of constant changes in the background due to brightness changes ormovements, such as for example in recordings of trees that stir in thewind or the motions of waves in the sea.

In this method, the frequency of the values of each individual pixelfrom the preceding images is incorporated in the calculation of abackground model. These values which have already been recorded areconsidered to be normally distributed random variables and representedas a Gaussian distribution. The different values of a pixel here receivedifferent Gaussian distributions. The probability of whether a pixel isto be observed is calculated from:

${P\left( X_{t} \right)} = {\sum\limits_{i = 1}^{K}\;{\omega_{i,t}*{\eta\left( {X_{t},\mu_{i,t},\Sigma_{i,t}} \right)}}}$

Here, K is the number of distributions, ω_(i) is the weighting and η(X,μi, t, Σi, t) is the Gaussian probability density function. New pixelvalues are then permanently compared to the Gaussian distributions untila hit is found. A hit is defined as a pixel value within the standarddeviation of 2.5. If no hit is found, the lowest fitting distribution isreplaced by the current distribution and adapted to the weighting. Inthis way, changes, such as light or vibrations, can be incorporated inthe background model.

In order to classify whether a pixel belongs to the foreground or thebackground image, the Gaussian distributions are sorted by theirprobabilities. The distributions with great probability that lie above athreshold value are considered to be background, and those that liebelow it are considered to be foreground.

Image Textures:

A suitable basis for detecting structures is “Laws Texture EnergyMeasurement” (Laws, 1980). It was developed with the aim of analysingthe structure in images and indicating how much energy it contains.Here, the image is convolved with a 5×5 convolution mask to highlightstructures. The following convolution formula is used for theconvolution of an image:

$b_{kl} = {\sum\limits_{i = {- \infty}}^{\infty}\;{\sum\limits_{j = \infty}^{\infty}\;{a_{ij}h_{{i - k},{j - l}}}}}$

In concrete terms convolution means that each point k, is targeted inthe input image with a convolution mask. At each point within the mask,the product is formed, added up and subsequently written in the outputimage at the location k.

The convolution mask used by Laws is calculated from different vectorsthat can be combined in each case for corresponding image contents.

L5 (Level)=[1 4 6 4 1]

E5 (Edge)=[−1 −2 0 2 1]

S5 (Spot)=[−1 0 2 0 −1]

R5 (Ripple)=[1 −4 6 −4 1]

The L5 vector calculates the local average, E5 detects edges, S5 detectsspots and R5 is suitable for wave-type structures. When forming theproduct from two of these vectors, the result is a two-dimensionalconvolution mask that convolves an output image according to therespective vector properties, and, after subsequent binarization, abinary image showing only the structures in the image is obtained.

Face Detection:

In face detection, digital images are analysed for different features todetect faces. One of the most common methods is OpenCV implementationbased on pattern recognition with training data, described in “RapidObject Detection using a Boosted Cascade of Simple Features” publishedby Paul Viola and Michael Jones (Viola & Jones, 2001). This algorithmpermits quick calculation with a low error rate, which means that robustdetection is also possible in near-real time.

In pattern recognition for faces according to Viola and Jones, initiallytraining data must be produced. In the course of this, both positiveimages, that is to say images that show faces, and negative images, i.e.images without faces, are used. For analysis purposes, a convolutionwith Haar wavelets with different scalings is performed (convolution see0 image textures). Haar wavelets are simple convolution masks formedfrom rectangular functions, see convolution masks 40 a,b (Viola & Jones,2001) in FIG. 1 as used in an imaged presentation of a person 10. In theconvolution, the sum of the black rectangles is subtracted from the sumof the white rectangles in a section of 24×24 pixels. To optimize thisprocedure, the calculations are performed with a summed area table. In asummed area table, the individual pixel values are added up. It ishereby possible to obtain the sum of the individual sections in onlyfour mathematical operations. In order to find only the useful valuesfrom the multiplicity of calculated values, Viola and Jones use theAdaBoost algorithm for machine learning. Here, classifiers are createdfrom positive and negative images and the features calculated therein.In the extended adaptive method, simple classifiers are furthermorecombined into one.

It is then possible to detect faces using the training data that havebeen generated. To this end, the image section runs through a cascade ofclassifiers that decide, on the basis of the calculated feature data,whether this is a negative image (“F”) or a positive image (“T”). FIG. 2shows a cascade (“1”, “2”, “3”) of the classifiers (Viola & Jones, 2001)with step A “All Sub-Windows”, step B “Further Processing” and step C“Reject Sub-Window”.

Test Setup:

Development Environment:

For implementing the system, the programming language C++ is used in thedevelopment environment Eclipse. The implementation of the imageprocessing is realized by the framework OpenCV. Tutorials from thedocumentation of OpenCV form the basis for the AKAZE and differenceimage method. (Alcantarilla, Nuevo, & Bartoli, 2012) (How to UseBackground Subtraction Methods, 2017).

Setup:

A test setup according to FIG. 3 (schematic) and FIG. 4 (view) is usedfor testing the system. The figure contains a target area 2, in thepresent case a projection area on a bin or a panel in an interior 20, inthe present case a passenger cabin, of a vehicle 22, in the present casean aircraft. The setup also contains a camera 4 in the form of a webcam,a light source 6 in the form of a projector and a computer 8, in thepresent case a notebook. The camera 4 is arranged next to the lightsource 6 or the projector. The projector produces light 24 within a beamcone 26 and thereby the image 14 b (projected onto the target area 2).Said image exhibits a specific image content 15, in the present case alandscape with sky. The camera 4 captures the image 14 b produced (orthe entire beam cone 26 that is incident on the target area 2) and avicinity 17, that is to say an additional, larger evaluation region. Thecamera 4 thereby captures in its field of view 30 a monitoring section28 of the beam cone 26. The camera 4 in this respect records a cameraimage 32.

The computer 8 controls the projector and produces the video signal andevaluates the camera image 32 using machine person detection todetermine whether a person 10 or part of said person is located in thebeam cone 26. Test devices are the LED projector Optoma ML750ST and thewebcam Logitech C920. These are raised to be level with a bin (targetarea 2) in a test arrangement using two stands and directed at it. It isimportant to note here that the autofocus of the camera is deactivated.During the development phase, videos are recorded for testing purposes.Here (indicated by arrow 12), a person 10 passes through the projectionand, during that time, the projector 6 is switched off and on again(entirely or locally) or switched to glare-free. When testing thesystem, the projector 6 as an extended screen and the webcam 4 areconnected to a laptop (computer 8), on which the programming code isexecuted. The window produced by OpenCV is opened in full-frame mode onthe extended screen for the contents that are to be presented on theprojector 6.

Upon detection of a part of the person 10 in the beam cone 26, theregion 18 thereof, in which the part of the person 10 is located, isswitched to glare-free, in the present case switched off. Rather thanthe image 14 a, a “black” image content of the brightness zero isreproduced in the corresponding region 18.

Solution Approaches:

Approach 1: Detecting within the Projected Image

In this approach, the detection of a person in the projection is to takeplace, specifically not when the person can be seen in the camera imagebut upon entry in the projection. To trigger the detection, matches areto be found between the projected image through the camera and thedigitally available image by way of the AKAZE feature descriptor anddetector. FIG. 5 shows the digitally available image 14 a and theprojected image 14 b, and also features 16 (indicated by circles) of theimage content 15, here the landscape with sky, which are matched on thebasis of arrows.

To use the AKAZE method, a threshold value with the number of matchesmust be set in the beginning. If a person then enters the projection,the number of the matching features falls below the threshold value andthe projector is switched off. Since no image is now available forcomparison purposes, a check is performed using the difference imagemethod as to whether the person is still located in the projection. FIG.6 shows a difference image in the projection. Since the image is blackwhen no person is located in the image, the average of the differenceimage is simply calculated and, if it increases, there is movement. Thebackground image for generating the difference image is created directlyafter the start of the application with a switched-off projector. Sincethe image section of the webcam extends beyond the projected area, it isadditionally necessary to create a mask such that only the projection istaken into account and not the region outside of it. This mask iscreated using the frame 42 in FIG. 5 of the AKAZE feature comparison.When the person leaves said region, the projection is activated againand the feature comparison mode is activated again.

Approach 2: Detecting Outside of the Projected Image

In this approach, an attempt is made to detect the person outside theprojection to deactivate the projector. This has the advantage that theanalysis proceeds independently of the projected image content andconsequently playback of moving image contents is also possible. This isto be realized using a difference image, feature detection—as anapproach 1—and filtering of the image using filter kernels according to“Laws Texture Energy Measurement”. To block the image content, it isnecessary—exactly as in approach 1—to start by creating a mask usingfeature detection. However, in this case the aim is to cover theprojected image content.

Difference Image:

First, the empty background image that is to be subtracted from thecurrent camera image must be recorded. This is done after the mask hasbeen created. Since the “mixture of Gaussian” method is applied, aplurality of images are used, as a result of which there is greaterrobustness with respect to minor fluctuations in the image content.Next, the average of the empty image is calculated so as to set athreshold value for the detection. From now on, the average of thedifference image will be continuously calculated. If a person enters theprojection, or the frame of the projection, the average of the entireimage increases to over the threshold value and the projector isdeactivated. This produces a difference image around the projection inaccordance with FIG. 7 (difference image method). In this figure, it canalso be seen, to the left and to the right of the projection, that thebrightness of the overall image is changed by switching off theprojection. An attempt is made to compensate this by learning the“mixture of Gaussian” method, which is intended to detect simplebrightness changes. If the person leaves the image, the value fallsunder the threshold and the projector is reactivated.

FIGS. 7 to 10 each show the same camera image 32, assessed/processedusing different methods. The person 10 presented is not part of theimage content 15 of the image 14 b, but is located in the beam cone 26as an actual person 10.

Features:

This method is based on the AKAZE feature detection used in approach 1.However, in this case the search is for the features outside of theprojection. Here, too, a threshold value in the empty image iscalculated at the beginning from the number of the features. If a personthen enters the camera image, the number of features changes. First,features that were found in the empty image are occluded, and second,more features are detected on account of the person.

Due to this change in features as compared to the threshold value, theprojector is deactivated. On account of the fact that the featuredetection is relatively robust with respect to changes in brightness,the fluctuation in brightness due to the projector being switched offhas no great influence on the number of the features 16. In this regard,see the feature image according to FIG. 8.

Structure:

In structure detection, only the structures in the image are to behighlighted using convolution as per “Laws Texture Energy Measurement”and subsequent binarization. Here, too, an average that is to be used asthe threshold value is calculated with the image without a person. Ifthis image is then changed by an entering person, the threshold beingexceeded deactivates the projector and, if the value falls below thethreshold, the projector is reactivated. See the structure image in FIG.9 (structure image method).

Approach 3: Face Detection:

Using face detection, the exact position of the person is to be detectedand thus, in contrast to the other approaches, it is not the entireprojector that is deactivated but only the region in which the face ofthe person is located. This is to be realized using the algorithm byViola and Jones. In this process, a cascade of classifiers isinitialized with training data based on Haar wavelets. Said algorithmthen examines the camera images for faces. If a face is found, it ismarked and the coordinates of the current position are received.

This position must now be converted with respect to the projected image.This requires the position of the projection in the camera image. Saidposition is calculated in the beginning using a projected test image andfeature detection, see FIG. 5. Using the position and size of the face,the projection and the ratio of projection image to projection in thecamera image, it is then possible to calculate the coordinates at whicha black circle is to be created.

//Position of the face

int FaceX=faces[i].x+faces[i].width*0.5;

int FaceY=faces[i].y+faces[i].height*0.5;

//Positions Projection

Point ProjectionLO=points[0][0];

Point ProjectionLU=points[0][3];

Point ProjectionRO=points[0][1];

Point Projection RU=points[0][2];

//Factor for scaling the circle

float factHori=(float)proje.cols/((float)ProjectionRO.x−(float)ProjectionLO.x);

float factVert=(float)proje.rows/((float)ProjectionRU.y−(float)ProjectionRO.y);

//New coordinates in projection

int FaceXnew=FaceX−ProjectionLO.x;

int FaceYnew=FaceY−ProjectionLO.y;

Using these coordinates, a circle is now created in the projected image,the position of which is recalculated for each individual image so as tocontinuously track the face and update the position in the projection.The result shows the face detection in FIG. 10.

Evaluation:

During testing of the different approaches, it has been found that inprinciple the objective of detecting persons in the projection,subsequently deactivating the projector and ultimately reactivating itwhen the person leaves the region has been met. In the feature detectionselected, that is to say when searching for features, the differenceimage method, the structure detection and in the case of face detection,there are, however, a few limitations.

Furthermore, it was found in tests that by switching the projector onand off, automatic brightness adaptation of the webcam is very lazy andmust be taken into account in the system.

Approach 1: Detecting within the Projected Image:

The AKAZE method in the projection offers the possibility of performinga feature comparison in the case of a moving image only with greatoutlay, because a new threshold value would have to be set for each newframe and at the same time a check would have to be performed as towhether a person is located in the projection. This would represent anenormous computational outlay, and a permanent comparison is probablydifficult on account of latency. Furthermore, the AKAZE method isdependent on the image content. The features are produced with the aidof structures located in the image. If the image available has littlestructure or the structures occur only in a specific region, detectioncan also only occur there. This problem can be explained on the basis ofFIG. 5. Matches in the image are found only in the lower image region(landscape), but not in the upper region (sky). A major advantage of theAKAZE method is that it is invariant with respect to brightness changesin the aircraft cabin.

The difference image method in principle also provides good results.However, this method is dependent on brightness changes. That is to say,when the light conditions in the cabin change compared to the backgroundimage that was generated in the beginning, this method no longer works.However, this background image is capable of learning and can beadapted.

One advantage of this approach is that the projector is deactivated onlywhen the person enters the projection.

Approach 2: Detecting Outside of the Projected Image:

The objective to not dazzle the person was also met in the case ofdetection around the projected image in all three solution approaches.This approach furthermore offers the possibility of video playback withlittle outlay.

Difference Image:

The learning rate is reliable and also has a good reaction time.However, the person when standing still is calculated into thedifference image and thus disappears. The attempt to bypass thisinvolves temporally limiting the learning rate both in the switched-onand in the switched-off projection mode. This also solves the problemthat the background is incorporated by calculation too “strongly” intothe background image when no person passes through the image for sometime, as a result of which the difference between the background withand without a person would be too great and the projector wouldconsequently not be switched on again. Furthermore, limitations thatfalsify the difference image occur due to shadows cast by the person anddue to brightness changes when switching the projector on and off.Brightness differences in the played-back video contents result inslight differences in the camera image, but these can be compensated.Furthermore, major brightness changes in the aircraft cabin result inthe projector being switched off because the difference to thedifference image becomes too great. This problem could be solved eitherby linking the light and projection system so that the projection systemcan be informed in the case of a change in light, or by an adaptation ofthe learning algorithm such that the latter recognizes the differentlight situations. Furthermore, the computational power in this method isrelatively low.

Features:

The feature detection operates with less reliability than the differenceimage, but does provide the desired result. However, the detection ishighly dependent on the structures of the person passing through theimage. For example, if said person is wearing a single-colour top,significantly fewer features are found than in the case of a topcarrying a logo or with a lot of structure. It is consequently difficultto find a meaningful threshold value, and it has been found that somepersons are not detected. Furthermore, the dependence with respect tobrightness changes is not as good as hoped.

On account of the changing brightness, new features arise instructure-rich image regions or are lost. However, as compared to thedifference image, homogeneous areas remain the same. In a measurement ofthe features found under the conditions as in FIG. 8, around 100features were found without person and approximately 200 with a person.This value can fluctuate for other persons. The computational power inthis method is relatively high.

Structure:

Structure detection provides the desired result only with greatlimitations. The change between an empty image and an image with aperson is not sufficiently great. The measurement of the average of thebinary image of these two states gave a value of approximately 30 with aperson and around 29 without a person. At values of 0 to 255, thisdifference is too small for reliable detection.

Approach 3: Face Detection:

In face detection, a very good result was obtained for the frontal face.The algorithm by Viola and Jones operates with great reliability andeven detects the face if it is located in the projection. However, nousable training data for detecting faces in profile could be found.Blocking out image regions also operates reliably, with the result thatthe reproduction of the video can be continued and regions are stillvisible. However, the calculation is somewhat more intensive, whichmeans that there is some latency between the real head position and thecalculated position of the black circle in the projection. Therefore, ifthe head moves quickly, it is possible that the person is dazzledbecause the black circle is not moved directly to the position of thehead. The resulting optical change on account of the projection in theface can result in no face being detected for a brief period of time.However, a problem in this approach arises when the projected imagecontent contains faces. The latter would be detected and blocked withouta person being located in the projection. This problem could beaddressed either by additional analysis of the projected image contentor by a combination with other approaches, such as, for example, thedifference image.

Overview of the Results:

Calcula- Stability/ tion detection Brightness Optimization Approachoutlay rate invariance approaches 1 within + + ∘ ∘ Permanent thresholdimage calculation Adaptive difference image 2.1 Outside − − + + −Adaptive difference Difference image linked to image: illumination 2.2Outside + − ∘ Features 2.3 Outside − − − ∘ Structure: 3 Face + + + +Better algorithm detection Pre-analysis of the image material on facesCombination with difference image

The best result would be achieved with a combination of face detectionand difference image method within the projection. The face detectionwould only be activated thereby if a person were actually located in theprojection. The difference image method used here would have to beadaptive for invariance with respect to brightness changes.

LIST OF REFERENCES

-   AKAZE and ORB planar tracking. (Sep. 4, 2016). Retrieved on Nov. 7,    2017 at    https://gregorkovalcik.github.io/opencv_contrib/tutorial_akaze_tracking.html-   Alcantarilla, P. F., Nuevo, J., & Bartoli, A. (October 2012). KAZE    FEATURES. Retrieved on Oct. 24, 2017 at    http://robesafe.com/personal/pablo.alcantarilla/kaze.html-   Chris Stauffer, W. G. (1999). Adaptive background mixture models for    real-time tracking. Cambridge.-   Erhardt, A. (2008). Einführung in die Digitale Bildverarbeitung.    Vieweg+Teubner.-   How to Use Background Subtraction Methods. (Nov. 7, 2017). Retrieved    on Nov. 8, 2017 at    https://docs.opencv.org/master/d1/dc5/tutorial_background_subtraction.html-   Kunz, D. (2016). Bildverarbeitung SS 20 chapter 2016. T H Köln.-   Kunz, D. (2016). Bildverarbeitung SS 2016 chapter 5. T H Köln.-   Laws, K. I. (1980). Rapid Texture Identification.-   Melton, B. (2015). Presentation on theme: Segmentation Using    Texture. Retrieved on Nov. 22, 2017 at    http://slideplayer.com/slide/6920161/-   Viola, P., & Jones, M. (2001). Rapid Object Detection using a    Boosted Cascade of Simple. Cambridge.

LIST OF REFERENCE SIGNS

-   2 Target area-   4 Camera-   6 Light source-   8 Computer-   10 Person-   12 Arrow-   14 a,b Image-   15 Image content-   16 Feature-   17 Vicinity-   18 Region-   20 Interior-   22 Vehicle-   24 Light-   26 Beam cone-   28 Monitoring section-   30 Field of view-   32 Camera image-   40 a,b Convolution mask-   42 Frame

What is claimed is:
 1. A method for avoiding dazzling of a person by a light source arranged in an interior of a vehicle, wherein the light source during operation emits light within a beam cone, in which: a camera is arranged in the interior and oriented such that at least one monitoring section of the beam cone, in which the person can enter, is located in the field of view of the camera, the camera records a camera image, using machine person detection, it is ascertained from the camera image whether at least one part of the person is located within the beam cone, in this case, at least the region of the beam cone in which the part of the person is located is switched to glare-free.
 2. The method according to claim 1, wherein the entire beam cone is switched to glare-free if at least one part of the person is located in the beam cone.
 3. The method according to claim 1, wherein the region of the beam cone is switched to glare-free by the light source for the region being switched to glare-free or switched off.
 4. The method according to claim 1, wherein the beam cone is directed at a target area of the interior and the camera is oriented such that at least a partial region of the target area lies within the field of view of the camera.
 5. The method according to claim 4, wherein the partial region of the target area captured by the camera is not taken into account in machine vision only with respect to image contents, produced by the light source, in the form of persons and/or movements and/or human-like image elements.
 6. The method according to claim 5, wherein the partial region of the target area, captured by the camera, is not taken into account in machine vision.
 7. The method according to claim 1, wherein the camera is oriented such that a vicinity of the beam cone is also located in the field of view of the camera.
 8. The method according to claim 1, wherein the camera is arranged next to the light source.
 9. The method according to claim 1, wherein machine vision and/or face detection and/or texture detection and/or movement detection is/are performed as part of the machine person detection. 